Work

DW-YOLO: A Model for Identifying Surface Characteristics and Distinguishing Grades of Graphite Ore

Year: 2025

Type: article

Abstract: Graphite’s critical role in modern industries necessitates efficient ore grade detection to optimize production costs and resource utilization. To overcome the limitations of traditional inspection systems in handling heterogeneous graphite ore samples with varying carbon content, we propose DW-YOLOv8—a YOLOv8s-based framework enhanced through three core innovations: (1) WIoU loss for dynamic anchor prioritization, (2) C2f_UniRepLKNetBlock for multi-scale feature extraction, and (3) the PAFPN for adaptive feature fusion. Evaluated on a dataset collected from the China Minmetals Heilongjiang graphite mine, the model achieves 93.88% mAP50, surpassing the baseline YOLOv8s by 9.6 percentage points. By balancing precision (9.6% improvement) and computational efficiency (9.4% lower Params), DW-YOLOv8 demonstrates robust deployment readiness for real-time industrial applications.

Source: Applied Sciences

DOI: 10.3390/app152111321

Authors: Xin Zhang, Xueyu Huang, Yuxing Yu

Institutions: Jiangxi University of Science and Technology, China Minmetals (China)

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,896